While the many robots in auto factories typically perform only one function, in the new Tesla factory in Fremont, Calif., a robot might do up to four: welding, riveting, bonding and installing a component.

At the Philips Electronics factory on the coast of China, hundreds of workers use their hands and specialized tools to assemble electric shavers. That is the old way.

At a sister factory here in the Dutch countryside, 128 robot arms do the same work with yoga-like flexibility. Video cameras guide them through feats well beyond the capability of the most dexterous human.

One robot arm endlessly forms three perfect bends in two connector wires and slips them into holes almost too small for the eye to see. The arms work so fast that they must be enclosed in glass cages to prevent the people supervising them from being injured. And they do it all without a coffee break — three shifts a day, 365 days a year.

All told, the factory here has several dozen workers per shift, about a tenth as many as the plant in the Chinese city of Zhuhai.

at Earthbound Farms in California, four newly installed robot arms with customized suction cups swiftly place clamshell containers of organic lettuce into shipping boxes. The robots move far faster than the people they replaced. Each robot replaces two to five workers at Earthbound, according to John Dulchinos, an engineer who is the chief executive at Adept Technology, a robot maker based in Pleasanton, Calif., that developed Earthbound’s system.

At an automation trade show last year in Chicago, Ron Potter, the director of robotics technology at an Atlanta consulting firm called Factory Automation Systems, offered attendees a spreadsheet to calculate how quickly robots would pay for themselves.

In one example, a robotic manufacturing system initially cost $250,000 and replaced two machine operators, each earning $50,000 a year. Over the 15-year life of the system, the machines yielded $3.5 million in labor and productivity savings.

Manufacturing Returns Without the Jobs

Manufacturing may be returning to the US, but the jobs are nowhere to be found. Moreover, the higher the salaries of workers, and the more benefits workers demand, the greater the incentives of manufacturers to eliminate humans.

The article notes that Apple plans to install a million robots in China to "supplement" its work force.

A large banner at Flextronics plant near San Francisco proudly proclaims “Bringing Jobs & Manufacturing Back to California!” but the assembly line runs 24 hours a day, seven days a week with nearly all robots and few human workers.

China, NAFTA Not To Blame

If you want to blame something for the loss of manufacturing jobs, blame increased productivity, not China or NAFTA.

A couple of graphs will show what I mean.

Manufacturing Jobs

That graph makes it appear as if the decline began in 1980.The following chart suggests something else entirely.

Manufacturing Jobs as Percentage of All Jobs

Except for the spike in World War II, manufacturing has been in perpetual constant decline since the beginning of this data series.

One of the most interesting things about the graph above is that, if technology is the primary driver, then employment in China must inevitably follow the same path. In fact, there are good reasons to believe that manufacturing employment’s download slope will be significantly steeper for China. The U.S. had to invent the technology to make manufacturing more productive, while in many cases China only needs to import it from more developed nations. It is also true that China is beginning its journey at a time when information technology (which is the primary enabler of automation) is many orders of magnitude more advanced than in the 1950s when U.S. manufacturing employment was at its peak.

In the U.S. (as well as in other advanced countries), workers shifted out of manufacturing and into the service sector — which now accounts for the vast majority of jobs. Will China be able to pull off the same transition?

In the absence of consumer spending, China’s economy remains highly dependent on manufacturing exports and, especially, on fixed investment. An astonishing 50% of China’s GDP is driven by investment in things like factories, housing and infrastructure (the U.S. figure is around 15%). The problem is that all that investment has to ultimately pay for itself, and that happens via consumption. Once a factory is built it has to then produce something that gets sold at a profit. Homes, retail buildings and apartment complexes likewise have to be sold or rented out. Obviously, no economy can indefinitely invest anything like 50% of its output without eventually finding a way to get a positive return on that investment.

Achieving that return requires consumers — either at home or abroad. China continues to rely heavily on consumers in the U.S. and Europe, but that’s unlikely to be a sustainable formula for growth. The debt crisis and the resulting austerity is cutting into economic growth and consumer spending in both Europe and the U.S.

The real problem China faces is that it is late to the party. Just as it reaches its manufacturing employment zenith, it faces a potentially disruptive impact from automation technology. And that will happen roughly in parallel with similar transitions in the service sectors of the countries that currently consume much of its output. In the face of that, can China succeed in re-balancing its economy toward consumption, increasing personal incomes, and building a vibrant service sector to keep its population employed?

Since 1928, tissue samples have been screened for breast cancer by hand. Pathologists examine the tumor under a microscope and, by measuring a handful of cellular features, can produce a fairly accurate diagnosis and prognosis for the patient. C-Path replaces the human looking down the microscope and uses computer vision to look for the same cancerous indicators. Furthermore — and this is what makes C-Path so accurate — by looking at a large number of human-diagnosed samples, the system learns.

For example, one of the features that human doctors look for is the speed at which tumor cells divide by mitosis — through learning, C-Path might’ve discovered that mitosis isn’t actually the most accurate indicator.

Learning also allowed C-Path to discover new, cancer-related cellular factors — 6,642 in total — which it then used to diagnose and prognose new cancer patients with better accuracy than a human doctor. One of these indicators, related to the stroma (connective tissue between cells), was a completely new discovery — in other words, C-Path’s automated learning process just saved the lives of innumerable breast cancer patients around the world.

A new walking, talking robot from Japan has a female face that can smile and has trimmed down to 43 kilograms (95 pounds) to make a debut at a fashion show. But it still hasn't cleared safety standards required to share the catwalk with human models.

"Technologically, it hasn't reached that level," said Hirohisa Hirukawa, one of the robot's developers. "Even as a fashion model, people in the industry told us she was short and had a rather ordinary figure."

To add to the literal objectification of women, the robot will appear in the fashion show, naked, so that the public may come up with fun things the robot can do.

HRP-4C was designed to look like an average Japanese woman, although its silver-and-black body recalls a space suit. It will appear in a Tokyo fashion show — without any clothes — in a special section just for the robot next week.

The robotic framework for the HRP-4C, without the face and other coverings, will go on sale for about 20 million yen ($200,000) each, and its programming technology will be made public so other people can come up with fun moves for the robot, the scientists said.

The robot can apparently move enough of her body that human-robot sexual contact doesn't appear out of the question, and--just like a woman--she can show such emotions as "anger and surprise"

That was written in 2009. By now I am quite confident they have improved upon the "rather ordinary" figure.

Last year GameChanger produced nearly 400,000 accounts of Little League games. This year that number is expected to top 1.5 million. And the articles don’t read like robots wrote them:

Friona fell 10-8 to Boys Ranch in five innings on Monday at Friona despite racking up seven hits and eight runs. Friona was led by a flawless day at the dish by Hunter Sundre, who went 2-2 against Boys Ranch pitching. Sundre singled in the third inning and tripled in the fourth inning … Friona piled up the steals, swiping eight bags in all …

The grandparents of a Little Leaguer would find this game summary—available on the web even before the two teams finished shaking hands—as welcome as anything on the sports pages.

Stats Monkey

NPR reports on a program called Stats Monkey that can do the same thing.

Sample story generated by SportsMonkey from April 25, 2009:

UNIVERSITY PARK — An outstanding effort by Willie Argo carried the Illini to an 11-5 victory over the Nittany Lions on Saturday at Medlar Field.

Argo blasted two home runs for Illinois. He went 3-4 in the game with five RBIs and two runs scored.

Illini starter Will Strack struggled, allowing five runs in six innings, but the bullpen allowed only no runs and the offense banged out 17 hits to pick up the slack and secure the victory for the Illini.

The Illini turned the game into a rout with four in the ninth inning.

Strack got the win for Illinois. It was his fourth victory of the season. Strack allowed five runs over 6 2/3 innings. Strack struck out two, walked three and surrendered six hits.

Mike Lorentson suffered his sixth loss of the season for Penn State. He went four innings, walked none, struck out two, and allowed six runs.Illinois closer John Anderson got the final seven outs to record his second save of the season.

Hammond says StatsMonkey can do more than analyze Little League games.

"Our goal is to genuinely model human thought, intelligence, reason," he says. "I have to admit, we are doing it not only in sports; we're looking at what other realms we could apply this [technology] to."

Scholars studying Wikipedia tend to ignore or quickly dismiss the influence of bots on the site, even though of the top 30 most prolific editors on the site, 22 are bots. In fact, Wikipedia itself erases their contributions; according to Geiger, when a Wikipedia "user account is flagged as a bot, all edits made by that user disappear from lists of recent changes so that editors do not review them."

Got that? Changes made by Wikipedia robots are automatically approved. Changes may by human editors need review. I have to ask: by computer robots or humans?

Inquiring minds might be interested in the list of articles on robots that I accumulated recently.

I will have some thoughts on jobs, unemployment, rising productivity and issues related to robots and technology in a follow-up post in which I will answer the questions posed in the title of the article. For now, study some of the links and articles to form your own opinions.

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